Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	18,040
2	New Jersey	16,201
3	Massachusetts	12,103
4	Rhode Island	11,534
5	Connecticut	10,121
6	District of Columbia	9,735
7	Delaware	7,571
8	Louisiana	7,278
9	Illinois	7,144
10	Maryland	6,137
11	Nebraska	5,051
12	Pennsylvania	5,013
13	Michigan	5,004
14	Iowa	4,452
15	South Dakota	4,393
16	Indiana	4,052
17	Colorado	3,682
18	Mississippi	3,629
19	Virginia	3,359
20	Georgia	3,319
21	Kansas	2,748
22	New Mexico	2,700
23	New Hampshire	2,547
24	Minnesota	2,526
25	Washington	2,525
26	Tennessee	2,477
27	Alabama	2,319
28	North Dakota	2,310
29	Ohio	2,306
30	Nevada	2,189
31	Utah	2,161
32	Florida	2,054
33	Wisconsin	2,035
34	California	1,949
35	Arizona	1,809
36	Missouri	1,721
37	Kentucky	1,696
38	North Carolina	1,639
39	South Carolina	1,632
40	Texas	1,620
41	Vermont	1,495
42	Arkansas	1,478
43	Idaho	1,336
44	Oklahoma	1,285
45	Wyoming	1,237
46	Maine	1,192
47	Oregon	839
48	West Virginia	807
49	Puerto Rico	795
50	Alaska	530
51	Hawaii	443
52	Montana	436

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Delaware	216
2	Massachusetts	197
3	Illinois	193
4	Rhode Island	190
5	District of Columbia	182
6	Nebraska	178
7	Connecticut	163
8	Maryland	161
9	Louisiana	128
10	New York	124
11	Iowa	120
12	New Jersey	118
13	Virginia	112
14	Minnesota	103
15	Mississippi	100
16	South Dakota	84
17	North Dakota	83
18	Indiana	79
19	Kansas	74
20	Pennsylvania	72
21	New Mexico	71
22	Wisconsin	70
23	Michigan	67
24	Arizona	65
25	Colorado	63
26	Alabama	61
27	Georgia	60
28	North Carolina	56
29	New Hampshire	55
30	Tennessee	55
31	Texas	53
32	California	49
33	Utah	49
34	Ohio	48
35	Nevada	46
36	Kentucky	42
37	Florida	34
38	Arkansas	33
39	Maine	31
40	South Carolina	31
41	Washington	31
42	Oklahoma	29
43	Puerto Rico	25
44	Missouri	24
45	Wyoming	23
46	Idaho	17
47	Oregon	14
48	West Virginia	12
49	Vermont	3
50	Alaska	2
51	Montana	1
52	Hawaii	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,426
2	New Jersey	1,141
3	Connecticut	921
4	Massachusetts	811
5	District of Columbia	521
6	Louisiana	512
7	Michigan	483
8	Rhode Island	452
9	Pennsylvania	346
10	Illinois	321
11	Maryland	316
12	Delaware	278
13	Indiana	251
14	Colorado	199
15	Mississippi	165
16	Georgia	147
17	Ohio	135
18	Washington	132
19	Minnesota	122
20	New Mexico	120
21	New Hampshire	116
22	Virginia	114
23	Nevada	112
24	Iowa	106
25	Alabama	98
26	Missouri	94
27	Arizona	89
28	Florida	89
29	Vermont	84
30	California	80
31	Kentucky	76
32	Wisconsin	76
33	South Carolina	73
34	Oklahoma	72
35	Kansas	64
36	Nebraska	63
37	North Carolina	62
38	North Dakota	55
39	Maine	51
40	South Dakota	49
41	Texas	44
42	Tennessee	42
43	Idaho	40
44	Puerto Rico	38
45	West Virginia	35
46	Arkansas	32
47	Oregon	32
48	Utah	24
49	Montana	14
50	Hawaii	12
51	Wyoming	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Jersey	23
2	Connecticut	22
3	Massachusetts	21
4	District of Columbia	15
5	Pennsylvania	13
6	Illinois	12
7	Delaware	11
8	Rhode Island	11
9	Colorado	8
10	Maryland	8
11	New York	8
12	Louisiana	7
13	Indiana	5
14	Michigan	5
15	New Mexico	5
16	Arizona	4
17	Iowa	4
18	Minnesota	4
19	Mississippi	4
20	New Hampshire	4
21	Ohio	4
22	Alabama	3
23	Nebraska	3
24	Virginia	3
25	California	2
26	Florida	2
27	Georgia	2
28	Missouri	2
29	Nevada	2
30	North Carolina	2
31	Kansas	1
32	North Dakota	1
33	South Carolina	1
34	South Dakota	1
35	Tennessee	1
36	Texas	1
37	Washington	1
38	West Virginia	1
39	Wisconsin	1
40	Alaska	0
41	Arkansas	0
42	Hawaii	0
43	Idaho	0
44	Kentucky	0
45	Maine	0
46	Montana	0
47	Oklahoma	0
48	Oregon	0
49	Puerto Rico	0
50	Utah	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	122,474	1	99
Dakota	Nebraska	74,503	2	99
Lincoln	Arkansas	73,096	3	99
Nobles	Minnesota	62,046	4	99
Lake	Tennessee	57,155	5	99
Davidson	Tennessee	5,774	248	92
Richland	South Carolina	2,877	597	80
Pierce	Washington	1,958	846	73
Orange	California	1,311	1167	62
York	South Carolina	997	1423	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Terrell	Georgia	2,813	2	99
Early	Georgia	2,748	3	99
Essex	New Jersey	1,890	4	99
Nassau	New York	1,842	5	99
Richland	South Carolina	144	496	84
Pierce	Washington	73	805	74
Davidson	Tennessee	62	899	71
Orange	California	26	1280	59
York	South Carolina	18	1431	54

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.